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Sparse Array Optimization Based on Modified Particle Swarm Optimization and Orthogonal Matching Pursuit

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This approach optimizes both element positions and excitations in antenna arrays, implying enhanced performance.

The proposed method achieves improved optimization performance through joint optimization of positions and excitations.
Key metrics indicate that the new approach significantly enhances the peak sidelobe level of the antenna array.
The methodology incorporates compressive sensing principles within a modified Particle Swarm Optimization framework.
This optimization strategy suggests a shift from traditional methods by considering the effects of element excitations.

Li et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

This approach optimizes both element positions and excitations in antenna arrays, implying enhanced performance.

The proposed method achieves improved optimization performance through joint optimization of positions and excitations.
Key metrics indicate that the new approach significantly enhances the peak sidelobe level of the antenna array.
The methodology incorporates compressive sensing principles within a modified Particle Swarm Optimization framework.
This optimization strategy suggests a shift from traditional methods by considering the effects of element excitations.

Li et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark